Multi-Expert Adversarial Attack Detection in Person Re ...

Protecting World Leaders Against Deep Fakes

Protecting World Leaders Against Deep Fakes Shruti Agarwal and Hany Farid University of California, Berkeley Berkeley CA, USA {shrutiagarwal, hfarid}@berkeley.edu

What Have We Learned From Deep Representations for …

what these powerful models actually have learned. In this paper we shed light on deep spatiotemporal net-works by visualizing what excites the learned models us-ing activation maximization by backpropagating on the in-put. We are the first to visualize the hierarchical features

  What, Have, Learned, What we have learned, Have learned

Finding Tiny Faces in the Wild With Generative Adversarial ...

faces, which are unfriendly for the face classifier. Toward-s this end, we design a refinement sub-network to recover some detailed information. In the discriminator network, the basic GAN [17, 12, 8] is trained to distinguish the real and fake high resolution images. To classify faces or non-

  Faces

Squeeze-and-Excitation Networks - openaccess.thecvf.com

Squeeze-and-Excitation Networks Jie Hu1∗ Li Shen2∗ Gang Sun1 hujie@momenta.ai lishen@robots.ox.ac.uk sungang@momenta.ai 1 Momenta 2 Department of Engineering Science, University of Oxford Abstract Convolutional neural networks are built upon the con-

  Network, Excitation, Squeeze and excitation networks, Squeeze

RegularFace: Deep Face Recognition via Exclusive ...

RegularFace: Deep Face Recognition via Exclusive Regularization Kai Zhao Jingyi Xu Ming-Ming Cheng ∗ TKLNDST, CS, Nankai University kaiz.xyz@gmail.com cmm@nankai.edu.cn

  Zhao

Auto-DeepLab: Hierarchical Neural Architecture Search for ...

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation Chenxi Liu1∗, Liang-Chieh Chen 2, Florian Schroff2, Hartwig Adam2, Wei Hua2, Alan Yuille1, Li Fei-Fei3 1Johns Hopkins University 2Google 3Stanford University Abstract Recently, NeuralArchitectureSearch(NAS)hassuccess-

PointNet: Deep Learning on Point Sets ... - CVF Open Access

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Qi* Hao Su* Kaichun Mo Leonidas J. Guibas Stanford University

  Open, Learning, Points, Deep, Sets, Pointnet, Deep learning on point sets

Frustum PointNets for 3D Object Detection From RGB-D Data

Frustum PointNets for 3D Object Detection from RGB-D Data Charles R. Qi1∗ Wei Liu2 Chenxia Wu2 Hao Su3 Leonidas J. Guibas1 1Stanford University 2Nuro, Inc. 3UC San Diego Abstract In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.

Class-Balanced Loss Based on Effective Number of Samples

and large-scale datasets including ImageNet and iNatural-ist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve signifi-cant performance gains on long-tailed datasets. 1. Introduction The recent success of deep Convolutional Neural Net-works (CNNs) for visual recognition [26, 37, 38, 16] owes

  Scale, Visual, Recognition, Visual recognition

ESRGAN: Enhanced Super-Resolution Generative Adversarial ...

ESRGAN: EnhancedSuper-Resolution Generative Adversarial Networks Xintao Wang 1, Ke Yu , Shixiang Wu2, Jinjin Gu3, Yihao Liu4, Chao Dong 2, Yu Qiao , and Chen Change Loy5 1 CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong 2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 3 The Chinese University of Hong Kong, …

  Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks

Building Automation & Control Systems

The Field device level are the physical input sensors and ... security and facility professional lack robust understanding of BACS vulnerabilities. In contrast, ... This group rated higher criticality of attacks against the Automation equipment and its network, demonstrating that they hold a higher level of BACS technical understanding that can ...

  Physical, Robust, Attacks

The Physical Protection of Critical Infrastructures and ...

The September 11 attacks demonstrated our national-level physical vulnerability to the threat posed by a formidable enemy-focused, mass destruction terrorism. The events of that day also validated how determined, patient, and sophisticated—in both planning and execution—our terrorist enemies have become. The

  Physical, Attacks

Marvell 88Q4364 Automotive 802.3ch compliant 10GBase …

implements the Ethernet physical layer portion of 2.5G/5G/10GBASE-T1 as defined by the IEEE 802.3ch standard. The 88Q4364 has integrated MACsec that secures against the Layer 2 In-Vehicle Networking security threats. MACsec secures data exchange on a hop-by-hop basis and prevents attacks such as intrusion, man-in-the-middle, and replay attacks.

  Physical, Complaints, Automotive, Attacks, Marvell, 10gbase, Marvell 88q4364 automotive, 88q4364, Compliant 10gbase

Ouroboros: A Provably Secure Proof-of-Stake Blockchain ...

of physical resources (e.g., proof of work). We also present a novel reward mechanism for in-centivizing proof of stake protocols and we prove that, given this mechanism, honest behavior is an approximate Nash equilibrium, thus neutralizing attacks such as selfish mining. We also

  Physical, Attacks, Ouroboros

Automotive Gateway: A Key Component to Securing the ...

NXP Semiconductors is the world leader in vehicle network (gateway) processors and In-Vehicle Networking (IVN), providing about half of all network connections found in a new car. CAN, LIN, FlexRay, and Ethernet networks with robust processors tie everything together, ensuring reliable, safe, and secure communication between

  World, Robust

Leading the IoT - Gartner

that reflect precisely the real physical conditions of each component of the trains. Life indicators typically measure the expected wear of components by counting relevant parameters such as cycles, time, distance and energy. Health indicators measure the actual status of component operation, such as the closing time for a door or the

  Physical, Gartner

A Guide to Crypto Custody - Gemini

keeping cash in your physical wallet or locked in a drawer, poses its own risks. There is no third-party involved to manage that risk (or your funds) if you were to lose access to your keys, experience a destructive event like a fire or power outage, or pass away unexpectedly. 2. Partial custody: “Wallet plus” solution

  Guide, Physical, Custody, Crypto, Guide to crypto custody